Begin by setting up Apache Spark, as it natively integrates with Apache Iceberg. Download and install Apache Spark if not already installed. Ensure that you have Spark version 3.0 or later, as it includes built-in support for Iceberg. Configure Spark with the necessary Iceberg dependencies by adding the Iceberg JAR files to the Spark classpath.

Ensure your JSON data file is properly formatted. The file should contain JSON objects that are consistently structured. Validate the JSON file to ensure there are no syntax errors or inconsistencies that could lead to issues during the data import process.

Set up an Iceberg table in a supported storage location, such as HDFS, S3, or a local file system. Use Spark SQL to define the schema and create an Iceberg table. For example, use the following command in Spark SQL to create a table:
```sql
CREATE TABLE my_iceberg_table (
id INT,
name STRING,
age INT
)
USING iceberg
LOCATION 'hdfs://path/to/your/iceberg/table'
```

Use Apache Spark to load the JSON data into a DataFrame. This can be done using Spark's `read` method, specifying the format as JSON, and providing the path to your JSON file:
```python
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("JSON to Iceberg") \
.getOrCreate()
json_df = spark.read.json("path/to/your/jsonfile.json")
```

Ensure that the DataFrame's schema matches that of your Iceberg table. If necessary, perform transformations on the DataFrame to align the fields, types, or structure. This could involve renaming columns, casting data types, or filtering out unnecessary columns.

Once the DataFrame is prepared, write it to the Iceberg table using the `write` method in Spark. Specify the Iceberg table as the target and use the `overwrite` or `append` mode as appropriate:
```python
json_df.write \
.format("iceberg") \
.mode("append") \
.save("hdfs://path/to/your/iceberg/table")
```

Finally, verify that the data has been successfully moved to the Iceberg table. Use Spark SQL to query the Iceberg table and validate that the data is correctly loaded and accessible:
```sql
SELECT * FROM my_iceberg_table LIMIT 10;
```
This query can be executed in Spark SQL to ensure the data is correctly stored and accessible in the Iceberg format.
By following these steps, you can effectively move data from a JSON file to Apache Iceberg using Apache Spark without needing any third-party connectors or integrations.